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1,773 results for “Supplementary data,”
Supplementary Information for "Biocatalytic Ether Lipid Synthesis by an Archaeal Glycerolprenylase" - Computational Data
<p>This is part of the external Supplementary Information covering the molecular dynamics simulations section for our publication "Biocatalytic Ether Lipid Synthesis by an Archaeal Glycerolprenylase" by Felix Kaspar et al., freely available as a preprint from ChemRxiv ( ).</p> <p>The .zip file contains raw data, processed data and metadata for the <strong>computational results</strong>. This includes files for the MD simulations and the preparations of the structures. </p> <p>Raw data regarding the experimental results are stored in a separate zenodo entry (<a href="../doi/10.5281/zenodo.10559443">https://zenodo.org/doi/10.5281/zenodo.10559443</a>) by Felix Kaspar. This includes UV, flourescence, NMR data, as well as data results of crystallization screens and reports on MPLC runs. </p>
Supplementary data from: A genetic assessment of natural barriers for isolating a proposed Greenback cutthroat trout reintroduction area
<p>We used genetic techniques to evaluate a series of natural waterfalls for their potential to serve as barriers to prevent nonnative salmonids from entering a proposed reintroduction area for federally threatened Greenback cutthroat trout <em>Oncorhynchus clarkii stomias</em>. Genetic samples were collected from nonnative Brook Trout <em>Salvelinus fontinalis</em> at 11 sampling reaches above and below natural waterfalls (height: ~1-3 m under baseflow conditions) along a 33-km segment of Colorado's upper Cache la Poudre River near the outflow of the proposed reintroduction area. To evaluate whether upstream movement of Brook Trout is restricted by any of these waterfalls, we characterized longitudinal trends in genetic diversity along the river corridor and examined patterns of genetic differentiation and population structure in relation to waterfall locations using a panel of microsatellites. We found no evidence that the waterfalls served as complete movement barriers for nonnative Brook Trout based on genetic clustering analyses, estimates of population differentiation, and longitudinal genetic patterns. Our multi-locus assessment did not identify alleles restricted to downstream reaches, and the river segment was genetically homogenized. This evaluation suggests that the existing waterfalls do not fully prevent upstream movement by nonnative Brook Trout, and thus barrier modification would be needed to establish an isolated Greenback cutthroat trout population in the proposed wilderness area.</p>
Supplementary data to the paper "Large scale voxel-based FEM formulation for NMR relaxation in porous media"
<p>This repository contains supplementary data to the paper "Large scale voxel-based FEM formulation for NMR relaxation in porous media", which is being considered for publication. The files are:<br><br>**_data.zip - Binary image files;</p> <p>FEM_T2_**.csv - T2 simulation results using finite elements;</p> <p>FEM_**_inv.csv - T2 inversion for the finite element simulations;</p> <p>RW_T2_**.csv - T2 simulation results using random walk;</p> <p>RW_**_inv.csv - T2 inversion for the random walk simulations;</p> <p>local_digital_correction.c - Vector containing the local geometry correction factors, accessed integers formed by the 8-bit binary key of the node neighborhood;</p> <p>Where ** is the rock type:</p> <p>AC - Austin Chalk</p> <p>BS - Berea Stripe</p> <p>DP - Desert Pink</p> <p>IB - Idaho Brown</p> <p>Obs.: in order to obtain the relaxation curves the following parameters were utilized in the simulations:</p> <p>Bulk diffusivity: 2500 micro-m2/s (all rock samples) </p> <p>Bulk relaxation: 2.6 s (all rock samples)</p> <p>Voxel size: {1.0; 2,0; 0.9; 2.0} micro-m (for AC, BS, DP, IB respectively)</p> <p>Surface relaxivity: {23.3; 12.1; 12.3; 8.3} micro-m/s (for AC, BS, DP, IB respectively)</p>
Supplementary File 8; The full data set used for the analysis presented herein
Open the record for dataset details and reuse information.
Supplementary Material for "Investigating Software Development Teams Members' Perceptions of Data Privacy in the Use of Large Language Models (LLMs)"
<h3>ABSTRACT<strong>: </strong></h3> <p><strong>Context</strong>: Large Language Models (LLMs) have revolutionized natural language generation and understanding. However, they raise significant data privacy concerns, especially when sensitive data is processed and stored by third parties. <br><strong>Goal</strong>: This paper investigates the perception of software development teams members regarding data privacy when using LLMs in their professional activities. Additionally, we examine the challenges faced and the practices adopted by these practitioners. <br><strong>Method</strong>: We conducted a survey with 78 ICT practitioners from five regions of the country. <br><strong>Results</strong>: Software development teams members have basic knowledge about data privacy and LGPD, but most have never received formal training on LLMs and possess only basic knowledge about them. Their main concerns include the leakage of sensitive data and the misuse of personal data. To mitigate risks, they avoid using sensitive data and implement anonymization techniques. The primary challenges practitioners face are ensuring transparency in the use of LLMs and minimizing data collection. Software development teams members consider current legislation inadequate for protecting data privacy in the context of LLM use. <br><strong>Conclusions</strong>: The results reveal a need to improve knowledge and practices related to data privacy in the context of LLM use. According to software development teams members, organizations need to invest in training, develop new tools, and adopt more robust policies to protect user data privacy. They advocate for a multifaceted approach that combines education, technology, and regulation to ensure the safe and responsible use of LLMs.</p>
Supplementary data for the Eoles model
<p>Contains several demand and production hourly profiles to be used by the Eoles model developed at CIRED</p>
Supplementary Data: MPBoot: Fast phylogenetic maximum parsimony tree inference and bootstrap approximation
<p>Supplementary Data<br> MPBoot: Fast phylogenetic maximum parsimony tree inference and bootstrap approximation<br> Submitted to BMC Evolutionary Biology</p> <p>This record contains PANDIT based dataset and TreeBASE dataset (Nguyen et al. 2015) which are analyzed by different bootstrap methods in the study "MPBoot: Fast phylogenetic maximum parsimony tree inference and bootstrap approximation". The PANDIT based dataset (compressed in file data_pandit.tar.gz) is used to benchmark the accuracy of bootstrap estimates. The TreeBASE dataset (compressed in file data_treebase.tar.gz) is used to benchmark computing times and capability of finding the best-known MP scores. </p> <p>After being uncompressed, the PANDIT based dataset comprises two subdirectories corresponding to the simulated DNA and AA MSAs. They were generated by Seq-Gen (Rambaut and Grass 1997), where the model parameters and true tree were inferred from the original MSAs downloaded from the PANDIT database (Whelan et al. 2006).</p> <ul> <li>Inside "dna" subdirectory, there are 6,207 numbered directories corresponding to 6,207 DNA MSAs. Note that the numbering of these directories is not consecutive because we excluded MSAs where TNT or PAUP* runs did not finish. In each numbered directory N, there are three files: (1) data.N contains the simulated MSA in PHYLIP format; (2) model.N contains the best-fit model detected from the corresponding original MSA; (3) tree.N contains the tree (in Newick format) inferred from the corresponding original MSA. tree.N and model.N are used by Seq-Gen to simulate the MSA in data.N.</li> <li>The "aa" subdirectory is organized similarly for 6,165 AA MSAs.</li> </ul> <p>After being uncompressed, the TreeBASE dataset comprises 115 files corresponding to 115 MSAs. There are:</p> <ul> <li>70 DNA MSAs in PHYLIP format. These files follow the naming scheme dna_[number of sequences]_[number of sites].phy.</li> <li>45 protein MSAs in PHYLIP format. These files follow the naming scheme prot_[number of sequences]_[number of sites].phy.<br> </li> </ul>
Supplementary Data for risk analysis
<p>The files in this record contain data for risk analysis for real-time flood control operation of a multi-reservoir system using a dynamic bayesian network.</p> <p>The files consist of:</p> <ul> <li>Reservoir data and river flood routing parameters</li> <li>Code and results of the Monte Carlo simulations</li> <li>Code and results of the Bayesian network</li> </ul>
An Image-Based Gamut Analysis of Translucent Digital Ceramic Prints for Coloured Photovoltaic Modules: Supplementary Data
<p>Colouring the frontglass of PV modules via digital ceramic printing aids in concealing the PV when integrated into existing building façades as BIPV, while admitting sufficient light to produce electricity. This promotes the visual acceptance and adoption of PV as a source of renewable energy in urban environments. The effective colour of the PV laminate is a combination of the transparent colour on glass and the colour of the PV cells. This colour should ideally match the architect’s visual expectations in terms of fidelity, but also in terms of relative PV efficiency as a function of print density. In practice, these requirements are often contradictory, particularly for vivid colours, and the visual results may deviate significantly. This paper presents an objective analysis of how colours appear on PV frontglass laminated with a PV module, using an image-based colour acquisition process. Given a set of 1044 nominal colours uniformly distributed in the RGB colour space, each printed in 10 opacities, we quantify the range of effective colours observed when printed on glass and combined with PV, and their deviation from the nominals. Our results confirm that the effective colour gamuts are significantly constrainted and skewed, depending on the ink volume and glass finish used for printing. In particular, blue-magenta hues cannot be reliably rendered with this process. These insights can serve as guidelines for selecting target colours for BIPV that can be well approximated in practice.</p>
Dummy upload for arXiv:1506.09008 supplementary data
<p>###Preamble###</p> <p>This upload contains initialization files and data for simulations reported in:<br> https://arxiv.org/abs/1506.09008: Coarse-grained modelling of strong DNA bending II: Cyclization</p> <p>The initialization files allow a user to repeat the reported simulations using the oxDNA model. oxDNA is available for download from:<br> https://dna.physics.ox.ac.uk/index.php/Main_Page. <br> The use and meaning of the input and output files are documented extensively on this wiki. </p> <p>This is only a practice upload, and so only a small part of the total material is included.</p> <p>###Organisation### </p> <p>Simulations are organised by system type. Folder DXXCYY corresponds to simulation of a cyclization system with Nd = XX and Nbp=YY, with the meaning of these symbols given in the text referenced above. "_seq" indicates simulation of a specific sequence, as listed in Table S1 of the reference.</p> <p>For each system, a "closed" and an "open" folder are present. These correspond to the two windows of umbrella sampling that were performed separately. </p> <p>###Content###</p> <p>Within each folder are the necessary initialization files to run the simulations exactly as reported in the reference above, simply by calling oxDNA from within the folder, using "inputVMMC" as the input file.</p> <p>Also included are output files for a single realisation of the simulation. </p> <p>Note that the results in the reference above were all obtained from 5 independent replicas, using different initial conditions and different seeds. These can be (statistically) recreated simply by drawing random starting configurations from the single available traj_hist file. </p> <p><br> </p>
Supplementary Data: iterative Random Forests to discover predictive and stable high-order interactions
<p>This repository contains scripts and data for the simulations and case studies described in <em>iterative Random Forests to discover predictive and stable high-order interactions.</em></p>
Supplementary Data: Global analyses of Higgs portal singlet dark matter models using GAMBIT
<p>The files in this record contain data for the effective Higgs portal dark matter models considered in the <a href="http://gambit.hepforge.org/">GAMBIT</a> "Higgs portal" paper.</p>
The supplementary data for validation of paper <The effect of the biogenic isoprene emission around the megacity on ozone pollution in Beijing>
<p>The supplementary data of paper <<strong>The effect of the biogenic isoprene emission around the megacity on ozone pollution in Beijing>. The zip bag includes the data of four figures in paper with the format of Netcdf, and the more detail could be got by connecting with Hui Wang(wanghui6@mail.bnu.edu.cn)</strong></p>
Supplementary data for: Do Images of Biskyrmions Show Type-II Bubbles?
<p>This repository contains the micromagnetic simulation scripts for the publication <strong>Do Images of Biskyrmions Show Type-II Bubbles?</strong> by <em>J. C. Loudon, A. C. Harrison, D. Cortes-Ortuno, M. T. Birch, L. A. Turnbull, A. Stefancic, F. Ogrin, E. Burgos-Parra, A. Campbell, H. Popescu, M. Beg, O. Hovorka, H. Fangohr, P. A. Midgley, G. Balakrishnan and P. D. Hatton</em></p> <p>Simulations are based on the OOMMF code [1] and its Jupyter interface JOOMMF [2]</p> <p> </p> <p>An updated version of this repository can be found here:</p> <p> </p> <p>https://github.com/davidcortesortuno/paper_biskyrmions_bubbles</p> <p> </p> <p> </p> <p>[1] M. J. Donahue and D. G. Porter, <em>OOMMF Users Guide, Version 1.0</em>, Tech. Rep. (National Institute of Standards and Technology, Gaithersburg, MD, 1999).</p> <p>[2] M. Beg, R. A. Pepper, and H. Fangohr, <em>User interfaces for computational science: A domain specific language for OOMMF embedded in python</em>. AIP Advances 7, 056025 (2017), <a href="https://doi.org/10.1063/1.4977225">https://doi.org/10.1063/1.4977225</a>.</p>
Supplementary Data for "Structure−Activity Relationships that Identify Metal−Organic Framework Catalysts for Methane Activation"
<p>DFT-optimized structures, energies, and computed physicochemical properties of metal-organic frameworks that correspond with work in "Structure−Activity Relationships that Identify Metal−Organic Framework Catalysts for Methane Activation" (DOI: <a href="https://pubs.acs.org/doi/10.1021/acscatal.8b05178">10.1021/acscatal.8b05178</a>).</p>
Supplementary data for "An initial assessment of the value of Allam Cycle power plants with liquid oxygen storage in future GB electricity system"
<p>The code for the Unit Commitment & Economic Dispatch model that was used in this work is available at: https://gist.github.com/vitali87/20688c161d7b5ad598b5d52b524f4585</p> <p>Sample output data can be found in the "Example Outputs.zip" file. This corresponds to the case outlined in the article that simulates a system with 5 Allam Cycle plants without Liquid Oxygen Storage, for the winter test week.</p> <p>To run the UCED model:</p> <ul> <li>Download "UC AIMMS Allam Cycle Model" code from the github and save as an AIMMS project file.</li> <li>Save the file in a folder that contains all the necessary input datasets, found in the "Universal Inputs for UCED Model.zip" file, and the example outputs, found in the "Example Outputs.zip" file, which are to be overwritten. Do not change the name of the input or output files.</li> <li>Open the project and execute the following procedures: <ul> <li>"Main Initialisation" - to initialise the problem</li> <li>"Read from Excell" - to read data from the input files</li> <li>"Main Execution" - to begin running the problem</li> </ul> </li> <li>Once the run is complete, execute "Run External Procedure" to overwrite the output files with the new data.</li> </ul> <p>To change the test week:</p> <ul> <li>Open "Demand Profiles" in 'sets' and change the set definition. Enter "C1" for the winter week and "C21" for the summer week. Another week can alternatively be selected. For example, entering "C45" would allow the model to run with the weather and demand data from the 45th week in the year 2010. </li> <li>Save and close the set.</li> </ul> <p>To change the number of plants in the system:</p> <ul> <li>Open "PCCSGenerators" in 'sets' and change the set definition. To run with 5 Post Combustion Capture plants, end the list of generators after plant number 5 by commenting the remaining plants. This is done by using "!" after the 5th plant name in the string. Then save and close the set.</li> <li>Repeat the above step for the "ACGenerators" and "AirSeparationUnits" sets, to change the number of Allam Cycle plants in the system.</li> </ul> <p>To add or remove oxygen storage capability from the Allam Cycle plants:</p> <ul> <li>Open the "Main Initialisation" procedure.</li> <li>To run the model without oxygen storage: <ul> <li>make sure the following command is stated: "AC_ASU_coupled := 0;"</li> <li>save and close the procedure</li> </ul> </li> <li>To run the model with oxygen storage: <ul> <li>make sure the following is command is stated: "AC_ASU_coupled := 1;"</li> <li>make sure that the number, 'X', of "map_AC_to_ASU('Gas_CCS_AC_X') := 'ASU_X';" commands that are active matches the number of active Allam Cycle plants in the model</li> <li>save and close the procedure.</li> </ul> </li> </ul>
Supplementary data for "Additive and interactive cumulative effects on boreal landbirds: winners and losers in a multi-stressor landscape"
<p>Supplementary data for:</p> <p>Mahon, C. Lisa , Gillian L. Holloway, Erin M. Bayne, and Judith D. Toms. 2019. Additive and interactive cumulative effects on boreal landbirds: winners and losers in a multi-stressor landscape. Ecological Applications in press. <a href="https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/eap.1895">https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/eap.1895</a></p> <p>This file lists point count data selected for this analysis from the ECCC data available at <a href="https://www.canada.ca/en/environment-climate-change/services/oil-sands-monitoring/monitoring-biodiversity-alberta-oil-sands.html">https://www.canada.ca/en/environment-climate-change/services/oil-sands-monitoring/monitoring-biodiversity-alberta-oil-sands.html</a>, under the “Cause-effect migratory landbirds at regional scales” and “Species-at-risk migratory landbirds” project links.</p>
Supplementary data: Food product categories compared against National Act 26905 maximum sodium levels (n 864).
<p>Supplementary data: Food product categories compared against National Act 26905 maximum sodium levels (n 864). Article: Monitoring sodium content in processed foods in Argentina 2017-2018: compliance with national legislation and regional targets. (submitted for publication to Nutrients).</p>
Early-Universe Simulations of the Cosmological Axion: Supplementary Data
<p>Public release of the axion field state obtained through simulations of the post-inflationary QCD axion as performed in "Early-Universe Simulations of the Cosmological Axion," from <a href="https://arxiv.org/abs/1906.00967">arXiv:1906.00967v1[astro-ph.CO]</a>.</p> <p>This dataset should enable researchers to construct density fields for gravitational simulations for axion structure formation. Alternatively, it provides initial conditions for the further evolution of a realistic axion field configuration to arbitrary late times under classical field dynamics. For detailed instructions regarding the data formatting, units, and density field construction, see README.pdf.</p>
Supplementary Data for "A framework for the construction of generative models for mesoscale structure in multilayer networks"
<p>Supplementary Data for "A framework for the construction of generative models for mesoscale structure in multilayer networks"</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.